Title: Rapid identification model of mine water inrush sources based on extreme learning machine

Authors: Ya Wang; Mengran Zhou; Pengcheng Yan; Feng Hu; Wenhao Lai; Yong Yang; Yanxi Zhang

Addresses: College of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China; School of Computer and Information, Fuyang Teachers College, Fuyang, China ' College of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China ' College of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China ' College of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China ' College of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China ' School of Resources and Geosciences, China University of Mining and Technology, Xuzhou, China ' Xieqiao Coal Mine, Huainan Mining Group, Fuyang, China

Abstract: In the process of disaster prevention of coal mine water inrush, it is necessary to quickly and accurately identify the types of water inrush sources. Based on the high sensitivity, rapid and accurate monitoring characteristics of laser induced fluorescence technology, the fluorescence spectra of water samples were collected on the experimental platform of water sample detection. After pre-processing spectra and extracting features, the multi-classification learning model is established by the extreme learning machine (ELM) algorithm. In this paper, it determines the sigmoid function as hidden layer activation function, and obtains the optimal number of hidden layer nodes by the method of cross-validation. ELM is compared with the conventional neural network classification model in different part, such as the average time and the average classification accuracy. The average classification accuracy of ELM combined with principal component analysis is about 98% and 93% in the training and testing set respectively. And the classification learning time is greatly improved. Therefore, the model is more suitable for rapid and accurate classification of water inrush sources.

Keywords: mine water inrush; water sources identification; laser induced fluorescence spectra; principal component analysis; extreme learning machine.

DOI: 10.1504/IJWMC.2017.089318

International Journal of Wireless and Mobile Computing, 2017 Vol.13 No.4, pp.286 - 290

Received: 12 Jun 2017
Accepted: 16 Jul 2017

Published online: 11 Jan 2018 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article